from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-26 14:07:47.921297
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Thu, 26, Nov, 2020
Time: 14:07:51
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.7375
Nobs: 122.000 HQIC: -43.9659
Log likelihood: 1265.18 FPE: 3.48661e-20
AIC: -44.8061 Det(Omega_mle): 1.71584e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.673541 0.201319 3.346 0.001
L1.Burgenland 0.134020 0.090537 1.480 0.139
L1.Kärnten -0.304677 0.076023 -4.008 0.000
L1.Niederösterreich 0.028438 0.217008 0.131 0.896
L1.Oberösterreich 0.265832 0.179062 1.485 0.138
L1.Salzburg 0.129293 0.089198 1.450 0.147
L1.Steiermark 0.087857 0.127635 0.688 0.491
L1.Tirol 0.163167 0.084174 1.938 0.053
L1.Vorarlberg 0.018086 0.083026 0.218 0.828
L1.Wien -0.163301 0.172337 -0.948 0.343
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.682229 0.257165 2.653 0.008
L1.Burgenland -0.008622 0.115652 -0.075 0.941
L1.Kärnten 0.348970 0.097112 3.593 0.000
L1.Niederösterreich 0.099862 0.277206 0.360 0.719
L1.Oberösterreich -0.214785 0.228734 -0.939 0.348
L1.Salzburg 0.165266 0.113941 1.450 0.147
L1.Steiermark 0.192187 0.163041 1.179 0.238
L1.Tirol 0.132448 0.107523 1.232 0.218
L1.Vorarlberg 0.201428 0.106057 1.899 0.058
L1.Wien -0.570640 0.220143 -2.592 0.010
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.339867 0.086142 3.945 0.000
L1.Burgenland 0.107242 0.038740 2.768 0.006
L1.Kärnten -0.025445 0.032530 -0.782 0.434
L1.Niederösterreich 0.137228 0.092855 1.478 0.139
L1.Oberösterreich 0.264952 0.076619 3.458 0.001
L1.Salzburg -0.007522 0.038167 -0.197 0.844
L1.Steiermark -0.059904 0.054614 -1.097 0.273
L1.Tirol 0.096154 0.036017 2.670 0.008
L1.Vorarlberg 0.148462 0.035526 4.179 0.000
L1.Wien 0.008086 0.073741 0.110 0.913
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.204280 0.102227 1.998 0.046
L1.Burgenland 0.007539 0.045973 0.164 0.870
L1.Kärnten 0.036128 0.038604 0.936 0.349
L1.Niederösterreich 0.092431 0.110194 0.839 0.402
L1.Oberösterreich 0.348842 0.090925 3.837 0.000
L1.Salzburg 0.085465 0.045293 1.887 0.059
L1.Steiermark 0.196982 0.064811 3.039 0.002
L1.Tirol 0.026415 0.042742 0.618 0.537
L1.Vorarlberg 0.114576 0.042159 2.718 0.007
L1.Wien -0.113121 0.087510 -1.293 0.196
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.836376 0.221734 3.772 0.000
L1.Burgenland 0.057298 0.099718 0.575 0.566
L1.Kärnten -0.015245 0.083732 -0.182 0.856
L1.Niederösterreich -0.104079 0.239013 -0.435 0.663
L1.Oberösterreich 0.048215 0.197220 0.244 0.807
L1.Salzburg 0.040172 0.098243 0.409 0.683
L1.Steiermark 0.115627 0.140578 0.823 0.411
L1.Tirol 0.231457 0.092709 2.497 0.013
L1.Vorarlberg 0.036224 0.091445 0.396 0.692
L1.Wien -0.214906 0.189813 -1.132 0.258
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.202569 0.151975 1.333 0.183
L1.Burgenland -0.041233 0.068346 -0.603 0.546
L1.Kärnten -0.010442 0.057389 -0.182 0.856
L1.Niederösterreich 0.197484 0.163818 1.206 0.228
L1.Oberösterreich 0.391551 0.135173 2.897 0.004
L1.Salzburg -0.039359 0.067335 -0.585 0.559
L1.Steiermark -0.055263 0.096351 -0.574 0.566
L1.Tirol 0.197430 0.063542 3.107 0.002
L1.Vorarlberg 0.053698 0.062676 0.857 0.392
L1.Wien 0.115026 0.130096 0.884 0.377
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.314255 0.193209 1.627 0.104
L1.Burgenland 0.067006 0.086890 0.771 0.441
L1.Kärnten -0.079922 0.072961 -1.095 0.273
L1.Niederösterreich -0.125618 0.208266 -0.603 0.546
L1.Oberösterreich -0.122432 0.171849 -0.712 0.476
L1.Salzburg -0.000912 0.085604 -0.011 0.992
L1.Steiermark 0.382204 0.122494 3.120 0.002
L1.Tirol 0.534801 0.080783 6.620 0.000
L1.Vorarlberg 0.228249 0.079681 2.865 0.004
L1.Wien -0.187290 0.165394 -1.132 0.257
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.139984 0.222267 0.630 0.529
L1.Burgenland 0.019575 0.099958 0.196 0.845
L1.Kärnten -0.062919 0.083934 -0.750 0.453
L1.Niederösterreich 0.261650 0.239589 1.092 0.275
L1.Oberösterreich 0.005075 0.197694 0.026 0.980
L1.Salzburg 0.232145 0.098479 2.357 0.018
L1.Steiermark 0.159652 0.140916 1.133 0.257
L1.Tirol 0.048646 0.092932 0.523 0.601
L1.Vorarlberg 0.012151 0.091665 0.133 0.895
L1.Wien 0.196607 0.190269 1.033 0.301
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.645844 0.123237 5.241 0.000
L1.Burgenland -0.008236 0.055422 -0.149 0.882
L1.Kärnten -0.006981 0.046537 -0.150 0.881
L1.Niederösterreich -0.053253 0.132840 -0.401 0.689
L1.Oberösterreich 0.264442 0.109612 2.413 0.016
L1.Salzburg 0.006898 0.054602 0.126 0.899
L1.Steiermark 0.009553 0.078131 0.122 0.903
L1.Tirol 0.075865 0.051527 1.472 0.141
L1.Vorarlberg 0.189619 0.050824 3.731 0.000
L1.Wien -0.113081 0.105495 -1.072 0.284
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.080539 -0.058410 0.195687 0.235320 0.014322 0.065965 -0.134447 0.099274
Kärnten 0.080539 1.000000 -0.069558 0.173990 0.071175 -0.165149 0.173257 0.013495 0.276508
Niederösterreich -0.058410 -0.069558 1.000000 0.223326 0.061285 0.151833 0.064572 0.045766 0.343825
Oberösterreich 0.195687 0.173990 0.223326 1.000000 0.241418 0.260772 0.066794 0.056042 0.033257
Salzburg 0.235320 0.071175 0.061285 0.241418 1.000000 0.134176 0.040377 0.069479 -0.072570
Steiermark 0.014322 -0.165149 0.151833 0.260772 0.134176 1.000000 0.091739 0.091457 -0.208545
Tirol 0.065965 0.173257 0.064572 0.066794 0.040377 0.091739 1.000000 0.131819 0.084269
Vorarlberg -0.134447 0.013495 0.045766 0.056042 0.069479 0.091457 0.131819 1.000000 0.072980
Wien 0.099274 0.276508 0.343825 0.033257 -0.072570 -0.208545 0.084269 0.072980 1.000000